{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/make-dot","entry":"make_dot","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":11,"n_papers_ran":0,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":9,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":11,"n_places_pointer_only":0,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":9},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2109.14285","paper":"/paper/be-confident-towards-trustworthy-graph-neural","title":"Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration","date":"2021-09-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Diego999/pyGAT","path":"visualize_graph.py","file_url":"https://github.com/Diego999/pyGAT/blob/HEAD/visualize_graph.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bca7f86b2fb5bf67","mcp_get_code":{"code_sha256":"bca7f86b2fb5bf67"}},{"arxiv_id":"2006.12671","paper":"/paper/afdet-anchor-free-one-stage-3d-object","title":"AFDet: Anchor Free One Stage 3D Object Detection","date":"2020-06-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chowkamlee81/CentrePointNet","path":"det3d/visualization/netviz.py","file_url":"https://github.com/chowkamlee81/CentrePointNet/blob/HEAD/det3d/visualization/netviz.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"78f63746495b109f","mcp_get_code":{"code_sha256":"78f63746495b109f"}},{"arxiv_id":"2003.14166","paper":"/paper/pix2shape-towards-unsupervised-learning-of-3d","title":"Pix2Shape: Towards Unsupervised Learning of 3D Scenes from Images using a View-based Representation","date":"2020-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rajeswar18/pix2shape","path":"diffrend/torch/GAN/utils.py","file_url":"https://github.com/rajeswar18/pix2shape/blob/HEAD/diffrend/torch/GAN/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1708832ee9dc5d99","mcp_get_code":{"code_sha256":"1708832ee9dc5d99"}},{"arxiv_id":"1910.06922","paper":"/paper/connections-between-support-vector-machines","title":"Gradient penalty from a maximum margin perspective","date":"2019-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AlexiaJM/MaximumMarginGANs","path":"Code/pytorch_visualize.py","file_url":"https://github.com/AlexiaJM/MaximumMarginGANs/blob/HEAD/Code/pytorch_visualize.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"52a3f79b47513c42","mcp_get_code":{"code_sha256":"52a3f79b47513c42"}},{"arxiv_id":"1908.09492","paper":"/paper/class-balanced-grouping-and-sampling-for","title":"Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection","date":"2019-08-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"poodarchu/Det3D","path":"det3d/visualization/netviz.py","file_url":"https://github.com/poodarchu/Det3D/blob/HEAD/det3d/visualization/netviz.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"78f63746495b109f","mcp_get_code":{"code_sha256":"78f63746495b109f"}},{"arxiv_id":"1809.00397","paper":"/paper/visual-transfer-between-atari-games-using","title":"Visual Transfer between Atari Games using Competitive Reinforcement Learning","date":"2018-09-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sowmya-mp/rl_a3c_pytorch","path":"visualize.py","file_url":"https://github.com/sowmya-mp/rl_a3c_pytorch/blob/HEAD/visualize.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"1db353764a69ff91","mcp_get_code":{"code_sha256":"1db353764a69ff91"}},{"arxiv_id":"1803.06815","paper":"/paper/espnet-efficient-spatial-pyramid-of-dilated","title":"ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation","date":"2018-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sacmehta/ESPNet","path":"train/VisualizeGraph.py","file_url":"https://github.com/sacmehta/ESPNet/blob/HEAD/train/VisualizeGraph.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8456f0fd622b9df9","mcp_get_code":{"code_sha256":"8456f0fd622b9df9"}},{"arxiv_id":"1710.10903","paper":"/paper/graph-attention-networks","title":"Graph Attention Networks","date":"2017-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Anak2016/GAT","path":"visualize_graph.py","file_url":"https://github.com/Anak2016/GAT/blob/HEAD/visualize_graph.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bca7f86b2fb5bf67","mcp_get_code":{"code_sha256":"bca7f86b2fb5bf67"}},{"arxiv_id":"1612.01105","paper":"/paper/pyramid-scene-parsing-network","title":"Pyramid Scene Parsing Network","date":"2016-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kazuto1011/pspnet-pytorch","path":"draw_model.py","file_url":"https://github.com/kazuto1011/pspnet-pytorch/blob/HEAD/draw_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ca6ae0c751805e86","mcp_get_code":{"code_sha256":"ca6ae0c751805e86"}},{"arxiv_id":"1611.09482","paper":"/paper/fast-wavenet-generation-algorithm","title":"Fast Wavenet Generation Algorithm","date":"2016-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vincentherrmann/pytorch-wavenet","path":"visualize.py","file_url":"https://github.com/vincentherrmann/pytorch-wavenet/blob/HEAD/visualize.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fee2210d0cb90734","mcp_get_code":{"code_sha256":"fee2210d0cb90734"}},{"arxiv_id":"1611.06403","paper":"/paper/deep-outdoor-illumination-estimation","title":"Deep Outdoor Illumination Estimation","date":"2016-11-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CyxFTS/Scenne-illumination-estimation-from-single-image","path":"NeuralNetwork/visualize_net.py","file_url":"https://github.com/CyxFTS/Scenne-illumination-estimation-from-single-image/blob/HEAD/NeuralNetwork/visualize_net.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0311b99405df4c9d","mcp_get_code":{"code_sha256":"0311b99405df4c9d"}}]}